The very first thing to note is that Impala does not replace MapReduce or use MapReduce as a processing engine. Impala processes data much, much faster than MapReduce and is considered an alternative data-processing framework on Hadoop. Impala processes data stored at the Hadoop data storage layer using its open source in-memory processing framework, which does not have an overhead as MapReduce does. Impala bypasses MapReduce to have native access to data in HDFS using the distributed query engine designed specially for superfast data processing. As each Impala daemon processes data locally on DataNode, processing is fast due to little or no network latency. You must know the fact that MapReduce is an amazing distributed data-processing framework to process data directly in a distributed clustered environment on DataNodes; however, executing SQL statements through the MapReduce framework exhibits performance inefficiencies mainly due to disk access. Impala overcomes this inefficiency by processing data in memory. Impala runs side by side with MapReduce by using the same Hadoop core components and hardware infrastructure. As mentioned earlier and rephrased here again, Impala is faster because the data is processed in memory; therefore, the memory requirement for Impala-installed Hadoop clusters is comparatively higher.